CN118280536A - Clinical path optimization method based on big data - Google Patents
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- 238000005457 optimization Methods 0.000 title claims abstract description 30
- 239000003814 drug Substances 0.000 claims abstract description 40
- 238000012216 screening Methods 0.000 claims abstract description 35
- 238000004458 analytical method Methods 0.000 claims abstract description 19
- 238000013480 data collection Methods 0.000 claims abstract description 11
- 238000003745 diagnosis Methods 0.000 claims description 17
- 201000010099 disease Diseases 0.000 claims description 16
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 16
- 238000004364 calculation method Methods 0.000 claims description 12
- 239000000203 mixture Substances 0.000 claims description 7
- 238000013523 data management Methods 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 4
- 230000000844 anti-bacterial effect Effects 0.000 claims description 3
- 239000008280 blood Substances 0.000 claims description 3
- 210000004369 blood Anatomy 0.000 claims description 3
- 241000411851 herbal medicine Species 0.000 claims description 3
- 238000003384 imaging method Methods 0.000 claims description 3
- 238000003771 laboratory diagnosis Methods 0.000 claims description 3
- 238000010827 pathological analysis Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims 1
- 238000013499 data model Methods 0.000 abstract description 6
- 229940079593 drug Drugs 0.000 description 12
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- 230000009286 beneficial effect Effects 0.000 description 2
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- 238000007689 inspection Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000001356 surgical procedure Methods 0.000 description 2
- 230000009897 systematic effect Effects 0.000 description 2
- 238000007418 data mining Methods 0.000 description 1
- 208000035474 group of disease Diseases 0.000 description 1
- 230000007170 pathology Effects 0.000 description 1
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Abstract
The invention relates to the technical field of clinical path optimization methods, in particular to a clinical path optimization method based on big data, which comprises a data collection unit, a CHS-DRG grouping unit, a data screening unit, a medicine consumable service multi-level dimension classification unit, an index system building unit and a big data modeling analysis unit; the data collection unit is used for collecting basic data information of the patient, wherein the basic data information comprises settlement list data and expense detail data; the CHS-DRG grouping unit is used for screening case information, and screening cases according to the CHS-DRG grouping information, wherein the screening same ADRG groups are used as target groups; the data screening unit is used for screening the data sets meeting the conditions; the clinical path task based on the big data DRG is carried out from the aspect of clinical path optimization, the data model is optimized by comparing the standard values of medical institutions of the same level in the region, various indexes of the hospital are accurately positioned, and data support is provided for the clinical path optimization of the hospital.
Description
Technical Field
The invention relates to the technical field of clinical path optimization methods, in particular to a clinical path optimization method based on big data.
Background
Along with the rapid development of information technology, big data is permeated into various industries including medical field, and the big data provides powerful power for innovation and development of medical industry by mass data resources, high-efficiency processing capacity and accurate prediction capacity, especially in the aspect of clinical path optimization, the application of the big data gradually changes the traditional medical mode, and the medical efficiency and quality are improved.
As in the prior art issued patent CN104834826B, the invention discloses a method and system for establishing and optimizing clinical paths based on data mining and graph theory techniques, the method comprises: s1, respectively extracting data aiming at different diseases; s2, analyzing the data in the distributed database, and respectively setting each diagnosis and treatment stage point of the complete treatment course for different symptoms; s3, storing diagnosis and treatment stage points based on graph theory technology, wherein the diagnosis and treatment stage points at least comprise diagnosis and treatment stages, treatment information is needed to be done among all the diagnosis and treatment stages, and clinical pathology data of all the diagnosis and treatment stages are stored; s4, searching a typical path aiming at the disease by using a correlation algorithm of graph theory; s5, establishing a clinical path according to each diagnosis and treatment stage point and the diagnosis and treatment activities of each diagnosis and treatment stage point, and storing the clinical path into a clinical path database; s6, recording the application result and variation of the clinical path, and re-optimizing the clinical path if the clinical path has variation.
However, in the use of the system, the system is inconvenient to realize that a clinical department is taken as a dominant mode, a difference item reason is found according to a data analysis result, reasonable diagnosis and treatment items, medicines and consumable materials are selected, a clinical path is optimized, a cost structure is optimized, and purchasing cost of the medicines and the consumable materials is reduced, so that the effect of improving the operation capability of a hospital through a cost angle is reduced.
Disclosure of Invention
In order to solve the technical problems, the invention provides the clinical path optimization method based on big data DRG from the clinical path optimization angle, which is used for accurately positioning various indexes of a hospital by comparing standard values of medical institutions at the same level in a region, optimizing a data model, providing data support for the optimal clinical path of the hospital and promoting high-quality development of the hospital.
The invention relates to a clinical path optimization method based on big data, which comprises a data collection unit, a CHS-DRG grouping unit, a data screening unit, a medicine consumable service multi-level dimension classification unit, an index system building unit and a big data modeling analysis unit;
the data collection unit is used for collecting basic data information of the patient, wherein the basic data information comprises settlement list data and expense detail data;
the settlement list data is as follows:
1. Patient basic information table: medical institution name for medical treatment, department code for discharge and main information of the number of days of hospitalization;
2. diagnostic information table: primary diagnostic code, name, and other diagnostic code names;
3. Surgical information table: primary surgical code, name, other surgical code name, and date of surgery;
4. charge item table: 14 expense categories and expense amounts;
5. Fund pay table: various fund payment types and amounts;
The cost details are as follows: medical institution information, medical insurance catalog code, name, catalog category, unit price, quantity and amount information;
The CHS-DRG grouping unit is used for screening case information, and screening clinical cases according to the CHS-DRG grouping information, wherein the screening same ADRG groups are used as target groups; according to the basic information and the expense detail data information of the patient in the settlement list data, carrying out DRG grouping through a CHS-DRG1.1 national edition grouping rule, and subdividing the grouping into ADRG groups;
The data screening unit is used for screening the data sets meeting the conditions; screening the cases with ICD-10 and patient information in the same department for 2-60 days, and meeting the requirement of ADRG cases at the same time, thereby screening a data set meeting the conditions;
The medicine consumable service multi-level dimension classification unit is used for classifying medicine codes, consumable codes and medical service codes according to the national medical insurance agency information service coding standard specification and according to the first-level, second-level, third-level and fourth-level classification dimensions; the specific division standard is as follows: the medical service primary code is divided according to the first 2 bits of the national code, the medical service secondary code is divided according to the first 4 bits of the national code, the medical service tertiary code is divided according to the first 6 bits of the national code, the medical service quaternary code is divided according to the first 11 bits of the national code, and the medicine and consumable codes are divided according to the first 10 bits of the national code;
The index system building unit is used for carrying out association modeling on the screened data sets meeting the conditions through the data screening unit, and carrying out data modeling work on the standardized multi-level dimension division standard by the associated clinical specialist; wherein the basic indexes comprise: the number of cases, the average case cost, the average hospitalization day, the medicine proportion, the consumption proportion, the medical technology cost proportion, the medical service cost proportion, the average case personal self-payment amount, the individual self-rate, the average age and the total hospitalization day are subjected to data modeling according to basic indexes;
The big data modeling analysis unit builds a data modeling and data index system; after data modeling and a data index system are established, calculating the path project expense amount of each case of each medical institution, clustering each expense into high-dimensional data, performing systematic clustering according to a plurality of cases and multi-dimensional data, and respectively clustering according to 2-10 classes to check whether the data have obvious differences; (as shown in FIG. 5)
By checking the data distribution, the data can be mainly classified into four types, the four types of data are used for carrying out hospital attribute statistics, the contributions of different hospitals to the classification are checked, and whether individual hospitals have own unique groups or not is checked; the clinical path optimization method is based on the big data DRG clinical path subject, the standard values of medical institutions of the same level in the region are compared, the data model is optimized, various indexes of the hospital are accurately positioned, data support is provided for the optimal clinical path of the hospital, the high-quality development of the hospital is promoted, clinical departments are used as the leading part, different item reasons are found out according to data analysis results, reasonable diagnosis and treatment items, medicines and consumable materials are selected, the clinical path is optimized, the cost structure is optimized, the purchasing cost of the medicines and the consumable materials is reduced, more disease groups are used for the medicines and the consumable materials, the cost of single products is controlled, unnecessary examination and repeated examination are avoided, and the method is different from other optimization methods.
Preferably, the data screening unit further comprises a verification unit;
the checking unit is used for checking whether the expense detail data meets the condition or not by correlating the settlement list data with the expense detail data; and the accuracy of the data is improved.
Preferably, the system further comprises a thematic report generation unit;
The thematic report generating unit is used for generating a clinical path analysis report based on the big data clinical disease group; more reasonably distributes resources and provides better medical services for patients.
Preferably, the data sources of the data collection unit include case data of the same primary diagnosis and want to be of the ADRG sets of medical institutions of the same peer.
Preferably, the system further comprises a clinical data management unit;
the clinical data management unit realizes the comparison analysis of the same group of case cost according to the analysis result of the big data modeling analysis unit, and finds out the difference reason.
Preferably, the system further comprises a cost anomaly early warning unit;
The cost anomaly early warning unit sets a cost balance point of each environment in the information system through accounting of disease composition cost, and early warning is carried out once the cost exceeds the standard disease composition cost; the method is beneficial to medical institutions to know the variation condition of the disease composition cost in real time, more accurately evaluate the economy of different treatment schemes, provide powerful basis for decision makers, provide better medical service for patients, and contribute to improving the satisfaction degree and the trust degree of the patients and strengthen the medical machines.
Preferably, the index system building unit further comprises a multi-stage classification calculation index unit;
The multi-stage classification calculation index unit is used for carrying out multi-stage classification calculation on the average cost, the average number and the number of people, and the calculation result is used for data modeling.
Preferably, the multi-level dimension classification unit of the medicine consumable service further comprises a cost detail cost repartition statistical dimension unit;
The cost detail cost repartition statistical dimension unit performs division statistics according to a plurality of classification dimensions of comprehensive service class, pathological diagnosis cost, laboratory diagnosis cost, imaging diagnosis cost, non-operative treatment project cost, operative treatment cost, rehabilitation class, traditional Chinese medicine class, western medicine class, traditional Chinese medicine class, chinese herbal medicine class, consumable class, antibacterial medicine class and blood cost.
Compared with the prior art, the invention has the beneficial effects that: the clinical path optimization method is based on the big data DRG clinical path subject, the standard values of medical institutions of the same level in the region are compared, the data model is optimized, various indexes of the hospital are accurately positioned, data support is provided for the optimal clinical path of the hospital, the high-quality development of the hospital is promoted, clinical departments are used as the leading part, different item reasons are found out according to data analysis results, reasonable diagnosis and treatment items, medicines and consumable materials are selected, the clinical path is optimized, the cost structure is optimized, the purchasing cost of the medicines and the consumable materials is reduced, more disease groups are used for the medicines and the consumable materials, the cost of single products is controlled, unnecessary examination and repeated examination are avoided, and the method is different from other optimization methods.
Drawings
FIG. 1 is a schematic diagram of the system architecture of the present invention;
FIG. 2is a schematic diagram of the system architecture of the present invention;
FIG. 3 is a schematic diagram of a CHS-DRG packet information screening architecture;
fig. 4 is a schematic diagram of a CHS-DRG packet information structure;
FIG. 5 is a schematic diagram of big data modeling data distribution;
FIG. 6 is a diagram of drug code division;
FIG. 7 is a consumable code division diagram;
fig. 8 is a schematic illustration of healthcare code division.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. This invention may be embodied in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Examples
As shown in fig. 1 to 8, the clinical path optimization method based on big data of the invention comprises a data collection unit, a CHS-DRG grouping unit, a data screening unit, a multi-level dimension classification unit for medicine consumable service, an index system building unit and a big data modeling analysis unit;
the data collection unit is used for collecting basic data information of the patient, wherein the basic data information comprises settlement list data and expense detail data;
the settlement list data is as follows:
1. Patient basic information table: medical institution name for medical treatment, department code for discharge and main information of the number of days of hospitalization;
2. diagnostic information table: primary diagnostic code, name, and other diagnostic code names;
3. Surgical information table: primary surgical code, name, other surgical code name, and date of surgery;
4. charge item table: 14 expense categories and expense amounts;
5. Fund pay table: various fund payment types and amounts;
The cost details are as follows: medical institution information, medical insurance catalog code, name, catalog category, unit price, quantity and amount information;
the CHS-DRG grouping unit is used for screening case information, screening clinical cases according to the CHS-DRG grouping information, and screening a group of diseases with ADRG groups as a target crowd; according to the basic information and the expense detail data information of the patient in the settlement list data, carrying out DRG grouping through a CHS-DRG1.1 national edition grouping rule, and subdividing the grouping into ADRG groups;
The data screening unit is used for screening the data sets meeting the conditions; screening the cases of the same ICD-10, the same department of patient information and the same hospital stay for 2-60 days, and the cases meeting ADRG groups of diseases at the same time, thereby screening a data set meeting the conditions;
The medicine consumable service multi-level dimension classification unit is used for classifying medicine codes, consumable codes and medical service codes according to the national medical insurance agency information service coding standard specification and according to the first-level, second-level, third-level and fourth-level classification dimensions; the specific division standard is as follows: the medical service primary code is divided according to the first 2 bits of the national code, the medical service secondary code is divided according to the first 4 bits of the national code, the medical service tertiary code is divided according to the first 6 bits of the national code, the medical service quaternary code is divided according to the first 11 bits of the national code, and the medicine and consumable codes are divided according to the first 10 bits of the national code;
The index system building unit is used for carrying out association modeling on the screened data sets meeting the conditions through the data screening unit, and carrying out data modeling work on the standardized multi-level dimension division standard by the associated clinical specialist; wherein the basic indexes comprise: the number of cases, the average case cost, the average hospitalization day, the medicine proportion, the consumption proportion, the medical technology cost proportion, the medical service cost proportion, the average case personal self-payment amount, the individual self-rate, the average age and the total hospitalization day are subjected to data modeling according to basic indexes;
The big data modeling analysis unit builds a data modeling and data index system; after data modeling and a data index system are established, calculating the path project expense amount of each case of each medical institution, clustering each expense into high-dimensional data, performing systematic clustering on the multi-dimensional data according to a plurality of examples, and respectively clustering according to 2-10 classes to check whether the data have obvious differences; (as shown in FIG. 5)
By checking the data distribution, the data can be mainly classified into four types, the four types of data are used for carrying out hospital attribute statistics, the contributions of different hospitals to the classification are checked, and whether individual hospitals have own unique groups or not is checked;
The data screening unit further comprises a verification unit;
The checking unit is used for checking whether the expense detail data meets the condition or not by correlating the settlement list data with the expense detail data;
In the embodiment, the clinical path problem based on big data DRG is carried out from the clinical path optimization angle, the standard value of the medical institutions of the same level in the region is compared, the data model is optimized, various indexes of the hospital are accurately positioned, data support is provided for the optimal clinical path of the hospital, the high-quality development of the hospital is promoted, clinical departments are taken as the leading part, the reasons of different items are found out according to the data analysis result, reasonable diagnosis and treatment items, medicines and consumable materials are selected, the clinical path is optimized, the cost structure is optimized, the purchasing cost of the medicines and the consumable materials is reduced, more disease groups are used for the medicines and the consumable materials, the cost of single products is controlled, unnecessary inspection and repeated inspection are avoided, the method is different from other optimization methods, the cost is controlled for the hospital under the clinical path angle, the high-efficiency development of the medical treatment is guaranteed, and the operation capability of the hospital can be improved through the cost angle.
Examples
On the basis of the embodiment 1, the clinical path optimization method based on big data further comprises a thematic report generation unit;
the thematic report generating unit is used for generating a clinical path analysis report based on the big data clinical disease group;
the data sources of the data collection unit comprise case data which are diagnosed by the same main body of a plurality of medical institutions at the same level and are in the same ADRG groups;
the system also comprises a clinical data management unit;
The clinical data management unit realizes the comparison analysis of the same group of case cost according to the analysis result of the big data modeling analysis unit, and finds out the difference reason;
The system also comprises a cost abnormity early warning unit;
the cost anomaly early warning unit sets a cost balance point of each environment in the information system through accounting of disease composition cost, and early warning is carried out once the cost exceeds the standard disease composition cost;
the index system building unit further comprises a multi-stage classification calculation index unit;
The multi-stage classification calculation index unit is used for carrying out multi-stage classification calculation on the average cost, the average number and the number ratio of people, and the calculation result is used for data modeling;
the medicine consumable service multi-level dimension classification unit further comprises a cost detail cost repartition statistical dimension unit;
the cost detail cost repartition statistical dimension unit performs division statistics according to a plurality of classification dimensions of comprehensive service class, pathological diagnosis cost, laboratory diagnosis cost, imaging diagnosis cost, non-operative treatment project cost, operative treatment cost, rehabilitation class, traditional Chinese medicine class, western medicine class, traditional Chinese medicine class, chinese herbal medicine class, consumable class, antibacterial medicine class and blood cost;
In the embodiment, the accuracy of data is improved, resources are distributed more reasonably, better medical services are provided for patients, medical institutions can quickly find and solve difference reasons, potential risks are reduced, the medical institutions are helped to make more scientific and reasonable decisions, and the operation efficiency is improved.
The main functions realized by the invention are as follows: the clinical path problem based on the big data DRG is carried out from the clinical path optimization angle, the standard values of medical institutions of the same level in the region are compared, the data model is optimized, various indexes of the hospital are accurately positioned, data support is provided for the optimal clinical path of the hospital, the high-quality development of the hospital is promoted, the scheme is different from other optimization methods, the cost expense is controlled for the hospital in the clinical path angle, the efficient development of medical treatment is guaranteed, and the operation capability of the hospital can be improved through the cost expense angle.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that it will be apparent to those skilled in the art that modifications and variations can be made without departing from the technical principles of the present invention, and these modifications and variations should also be regarded as the scope of the invention.
Claims (8)
1. The clinical path optimization method based on big data is characterized by comprising a data collection unit, a CHS-DRG grouping unit, a data screening unit, a medicine consumable service multi-level dimension classification unit, an index system building unit and a big data modeling analysis unit;
the data collection unit is used for collecting basic data information of the patient, wherein the basic data information comprises settlement list data and expense detail data;
The CHS-DRG grouping unit is used for screening case information, and screening cases according to the CHS-DRG grouping information, wherein the screening same ADRG groups are used as target groups;
The data screening unit is used for screening the data sets meeting the conditions;
the medicine consumable service multi-level dimension classification unit is used for classifying medicine codes, consumable codes and medical service codes according to the national medical insurance agency information service coding standard specification and according to the first-level, second-level, third-level and fourth-level classification dimensions;
The index system building unit is used for carrying out association modeling on the screened data sets meeting the conditions through the data screening unit, and carrying out data modeling work on the standardized multi-level dimension division standard by the associated clinical specialist;
the big data modeling analysis unit builds data modeling and a data index system.
2. The big data based clinical path optimization method according to claim 1, wherein the data screening unit further comprises a verification unit;
the verification unit is used for verifying whether the expense detail data meets the condition by correlating the settlement list data with the expense detail data.
3. The big data based clinical path optimization method according to claim 1, further comprising a thematic report generation unit;
the thematic report generation unit is used for generating a clinical path analysis report based on the big data disease group.
4. The big data based clinical path optimization method of claim 1, wherein the data source of the data collection unit includes case data of a plurality of peer medical institutions.
5. The big data based clinical path optimization method according to claim 1, further comprising a clinical data management unit;
the clinical data management unit realizes the comparison analysis of the same group of case cost according to the analysis result of the big data modeling analysis unit, and finds out the difference reason.
6. The big data based clinical path optimization method according to claim 1, further comprising a cost anomaly early warning unit;
The cost anomaly early warning unit sets a cost balance point of each environment in the information system through accounting of disease composition cost, and early warning is carried out once the cost exceeds standard disease composition cost.
7. The big data based clinical path optimization method according to claim 1, wherein the index system construction unit further comprises a multi-stage classification calculation index unit;
The multi-stage classification calculation index unit is used for carrying out multi-stage classification calculation on the average cost, the average number and the number of people, and the calculation result is used for data modeling.
8. The big data based clinical path optimization method according to claim 1, wherein the pharmaceutical consumable service multi-level dimension classification unit further comprises a cost detail cost repartition statistical dimension unit;
The cost detail cost repartition statistical dimension unit performs division statistics according to a plurality of classification dimensions of comprehensive service class, pathological diagnosis cost, laboratory diagnosis cost, imaging diagnosis cost, non-operative treatment project cost, operative treatment cost, rehabilitation class, traditional Chinese medicine class, western medicine class, traditional Chinese medicine class, chinese herbal medicine class, consumable class, antibacterial medicine class and blood cost.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008047154A (en) * | 2007-10-19 | 2008-02-28 | Hitachi Ltd | Clinical path operation support information system |
| CN112397171A (en) * | 2020-12-04 | 2021-02-23 | 上海蓬海涞讯数据技术有限公司 | DRG-based method, device, processor and storage medium for realizing monitoring of critical path for diagnosis and treatment items and materials |
| CN114141377A (en) * | 2021-04-29 | 2022-03-04 | 深圳市康比特信息技术有限公司 | Method for establishing diagnosis rule base, method and equipment for checking diagnosis information |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008047154A (en) * | 2007-10-19 | 2008-02-28 | Hitachi Ltd | Clinical path operation support information system |
| CN112397171A (en) * | 2020-12-04 | 2021-02-23 | 上海蓬海涞讯数据技术有限公司 | DRG-based method, device, processor and storage medium for realizing monitoring of critical path for diagnosis and treatment items and materials |
| CN114141377A (en) * | 2021-04-29 | 2022-03-04 | 深圳市康比特信息技术有限公司 | Method for establishing diagnosis rule base, method and equipment for checking diagnosis information |
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